Algorithms for Discovery of Multiple Markov Boundaries
By Alexander R. Statnikov, Jan Lemeire, Constantin F. Aliferis. 2013
89 citations recorded by the source corpus.
Algorithms for Markov boundary discovery from data constitute an important recent development in machine learning, primarily because they offer a principled solution to the variable/feature selection problem and give insight on local causal structure. Over the last decade many sound algorithms have been proposed to identify a single Markov boundary of the response variable. Even though faithful distributions and, more broadly, distributions that satisfy the intersection property always have a single Markov boundary, other distributions/data sets may have multiple Markov boundaries of the response variable. The latter distributions/data sets are common in practical data-analytic applications, and there are several reasons why it is important to induce multiple Markov boundaries from such data. However, there are currently no sound and efficient algorithms that can accomplish this task. This paper describes a family of algorithms TIE* that can discover all Markov boundaries in a distribution. The broad applicability as well as efficiency of the new algorithmic family is demonstrated in an extensive benchmarking study that involved comparison with 26 state-of-the-art algorithms/variants in 15 data sets from a diversity of application domains.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Lu Zhang
- Xintao Wu
- Yongkai Wu
- Cynthia Rudin
- Aaron Fisher
- Francesca Dominici
- Ninareh Mehrabi
- Silvia Chiappa
Citations in the corpus
- Achieving Non-Discrimination in Data Release (2016, methodology): “Thus, we can use local causal discovery algorithms such as the Markov blanket [31] to determine the local structure for the decision attribute E.”
- All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018, background): “This common scenario has been called the “Rashomon” effect of statistics (Breiman et al., 2001; see also Lecué, 2011; Statnikov et al., 2013; Tulabandhula and Rudin, 2014; Nevo and Ritov, 2017; Letham et al., 2016).”